Extreme High-Gain Friction Observer of Flexible Joint Robots With L1 Adaptive Framework

Compliance control enables flexible joint robots (FJRs) to interact with unknown environments, but joint friction may significantly degrade control performance and backdrivability. While several model-free friction observers for FJRs have been studied in recent decades, current approaches still face challenges when the robot interacts with stiff environments. To tackle this, this paper proposes a new friction observer based on an $\mathcal{L}_1$ adaptive framework. The main advantage of the proposed method is that it overcomes a fundamental trade-off in the state-of-the-art (SOTA) method between accurate friction compensation and natural environmental interactions. Moreover, the proposed approach enables the use of extremely high gains, which yield several additional benefits. First, unlike the conventional methods, which require feedback of so-called nominal signals obtained through simulation, measured motor signals can be fed back into the controllers, leading to a simpler implementation. Second, we provide performance analysis showing that increasing the gain improves performance and results in near-zero steady-state error. Third, the observer's performance can be adjusted using only a single parameter. Lastly, the numerical issues arising from extremely high gains are alleviated by employing the stable numerical method. The above theoretical findings are validated through simulations, and the effectiveness of the proposed approach is further evaluated with real-world experiments using both single- and 7-joint FJR systems. The results demonstrated that the proposed approach enables robots to interact with stiff environments more naturally, while achieving enhanced friction compensation performance.

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Related Publications

"Extreme High-Gain Friction Observer of Flexible Joint Robots With L1 Adaptive Framework", T-RO 2026 (accepted)

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